1. CMPE 226 Database Systems February 21 Class Meeting
Department of Computer Engineering San Jose State University Spring 2017 Instructor: Ron Mak

2. Entity Integrity Constraint
No primary key column of a relational table can have null (empty) values.
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

3. Entity Integrity Constraint, cont’d
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

4. Foreign Keys
A foreign key is a column in a table that refers to a primary key column in another table.
In a relational schema, draw an arrow from the foreign key to the corresponding primary key.

5. Mapping 1:M Relationships
Mandatory
participation
on both sides.
The foreign key on the
M side of the 1:M relationship
corresponds to the primary key
on the 1 side.
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

6. Mapping 1:M Relationships, cont’d
Optional participation
on the 1 side.
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

7. Mapping 1:M Relationships, cont’d
Optional participation
on the M side.
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

8. Mapping 1:M Relationships, cont’d
Rename a foreign key
to better reflect the
role of a relationship.
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

9. Mapping M:N Relationships
Use the
bridge relation
BELONGSTO
with two
foreign keys.
AKA:
linking table
or join table
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

10. Mapping M:N Relationships, cont’d
Optional
participation
on both sides.
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

11. Mapping M:N Relationships, cont’d
with an
attribute.
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

12. Mapping 1:1 Relationships
Map 1:1 relationships similarly to 1:M relationships.
One table will have a foreign key pointing to the primary key of the other table.
It can be an arbitrary choice of which table has the foreign key.
Make the choice that is most intuitive or efficient.

13. Mapping 1:1 Relationships, cont’d
Table VEHICLE has the foreign key.
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

14. Referential Integrity Constraint
The value of a foreign key must either:
Match one of the values of the primary key in the referred table.
Be null.
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

15. Mapping Example #1: ER Diagram
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

16. Mapping Example #1: Relational Schema
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

17. Mapping Example #1: Sample Data
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

18. Mapping Candidate Keys
Choose one of the
candidate keys
to be the primary key.
Map the other
as non-primary.
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

19. Mapping Candidate Keys, cont’d
Generally choose
a non-composite
candidate key as
the primary key.
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

20. Mapping Multivalued Attributes
Map the multivalued
attribute as a new table.
This becomes a 1:M
relationship with the
new table on the M side.
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

21. Mapping Derived Attributes
Do not map derived attributes.
Sample data records.
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN
As seen by the front-end application.

22. Mapping Example #2: Attributes
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

23. Mapping Unary 1:M Relationships
The table contains a
foreign key that
corresponds to its
own primary key.
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

24. Mapping Unary M:N Relationships
Use a bridge relation
where both foreign keys
refer to the primary
key of the same table.
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

25. Mapping Unary 1:1 Relationships
Map similarly
to a unary 1:M
relationship.
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

26. Mapping Multiple Relationships
Map each
relationship.
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

27. Mapping Weak Entities
Map weak entities the same way you map regular entities.
The resulting table has a composite primary key that is composed of:
The partial identifier of the table.
The foreign key corresponding to the primary key of the owner table.

28. Mapping Weak Entities, cont’d
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN
The APARTMENT table’s composite primary key
consists of AptNo (the partial key)
and BuildingID (the foreign key
corresponding the the primary key
of the owner BUILDING table).

29. Mapping Example #3: ER Diagram
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

30. Mapping Example #3: Relational Schema
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

31. Mapping Example #3: Sample Data
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

32. Mapping Example #3: Sample Data, cont’d
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

33. Relational Database Constraints
A constraint is a rule that a relational database must satisfy in order to be valid.
Two types of constraints:
implicit constraints
user-defined constraints

34. Implicit RDB Constraints
Each relation (table) in a relational schema must have a unique name.
For each relation:
Each column name must be unique.
Each row must be unique.
Each column of each row must be single-valued.
Domain constraint: All values in a column must be from the same predefined domain.
The column order is irrelevant.
The row order is irrelevant.

35. Implicit RDB Constraints, cont’d
Primary key constraint: Each relation must have a primary key (a column or set of columns) whose value is unique for each row.
Entity integrity constraint: No primary key column can have a null value.
Referential integrity constraint: In each row containing a foreign key, either the value of the foreign key must match one of the values of the primary key of the referred table, or the foreign key is null.

36. User-Defined RDB Constraints
Optional attributes
Mandatory foreign key
Example: Each employee must report to a department.
Exact cardinalities
Example: A student can take at most 5 classes.
Business rules
Usually enforced by front-end applications.
Example: Each organization must have both male and female students.

37. User-Defined RDB Constraints, cont’d
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

38. ER Modeling and Relational Modeling
Why not skip ER modeling and go directly to logical modeling (creating relational schemas)?
ER modeling is better for visualizing requirements.
Certain concepts can be visualized graphically only in an ER diagram.
Every attribute appears only once in an ER diagram.
A conceptual model (ER diagram) is better for communication and documentation.

39. Break

40. Database Update Operations
Insert operation
Enter new data into a table.
Delete operation
Remove data from a table.
Modify operation
Change existing data in a table.

41. Update Anomalies Insertion anomaly Deletion anomaly
Modification anomaly

42. A Table Containing Redundant Data
Can you spot the data redundancies?
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

43. A Table Containing Redundant Data, cont’d
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

44. Update Anomalies, cont’d
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

45. Normalization Normalize tables to eliminate update anomalies.
Normalization is based on the concept of functional dependencies.

46. Functional Dependencies
Functional dependency: The value of one or more columns in each row of a table determines the value of another column in the same row.
Write A  B
Column(s) A functionally determines column(s) B.
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN
ClientId  ClientName

47. Functional Dependencies, cont’d
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

48. Functional Dependencies, cont’d
(Set 1) CampaignMgrID  CampaignMgrName
But not the reverse!
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

49. Functional Dependencies, cont’d
(Set 2) ModeID  Media, Range
But neither Media nor Range determine ModeID.
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

50. Functional Dependencies, cont’d
(Set 3) AdCampaignID  AdCampaignName, StartDate, Duration,
CampaignMgrId, CampaignMgrName
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

51. Functional Dependencies, cont’d
(Set 4) AdCampaignName  AdCampaignID, StartDate, Duration,
CampaignMgrID, CampaignMgrName
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

52. Functional Dependencies, cont’d
(Set 5) AdCampaignID, ModeID  AdCampaignName, StartDate, Duration,
CampaignMgrID, CampaignMgrName,
Media, Range, BudgetPctg
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

53. Functional Dependencies, cont’d
(Set 6) AdCampaignName, ModeID  AdCampaignID, StartDate, Duration,
CampaignMgrID, CampaignMgrName,
Media, Range, BudgetPctg
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

54. Trivial Functional Dependencies
Remove trivial function dependencies (FDs):
A  A
A, B  A, B
A, B  A

55. Augmented Functional Dependencies
If A  B, then remove augmented FDs such as A, C  B
Because replace with
(Set 2) ModeID  Media, Range
(Set 3) AdCampaignID  AdCampaignName, StartDate, Duration,
CampaignMgrId, CampaignMgrName
(Set 5) AdCampaignID, ModeID  AdCampaignName, StartDate, Duration,
CampaignMgrID, CampaignMgrName,
Media, Range, BudgetPctg
(Set 5) AdCampaignID, ModeID  BudgetPctg

56. Equivalent Functional Dependencies
Equivalent FDs:
A  B B  A
A  B, X B  A, X
Y, A  B, X Y, B  A, X
Remove all but one from each equivalence set.

57. Equivalent Functional Dependencies
Because are equivalent:
FD Sets 3 and 4 are equivalent: Remove Set 4
FD Sets 5 and 6 are equivalent: Remove Set 6
AdCampaignID  AdCampaignName AdCampaignName  AdCampaignID
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

58. Streamlined Functional Dependencies
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

59. Partial Functional Dependency
A table column is functionally dependent on a component of a composite primary key.
(Set 2) ModeID  Media, Range
(Set 3) AdCampaignID  AdCampaignName, StartDate, Duration,
CampaignMgrId, CampaignMgrName
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

60. Full Key Functional Dependency
A table column is functionally dependent on the primary key and not partially dependent on any separate component of the primary key.
(Set 5) AdCampaignID, ModeID BudgetPctg
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

61. Transitive Functional Dependency
A nonkey column is functionally dependent on another nonkey column.
CampaignMgrID  CampaignMgrName
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

62. Another Functional Dependencies Example
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

63. Normalization Improve the design of database tables.
Eliminate update anomalies.
Three normal forms.
First normal form (1NF)
Second normal form (2NF)
Third normal form (3NF)
From lower to higher, each normal form has increasingly stricter conditions.
Even higher normal forms mostly of theoretical value.
Boyce-Codd (BCNF), 4NF, 5NF, domain key (DKNF).

64. First Normal Form (1NF) A table is in 1NF if:
Each row is unique.
All values in a column must be from the same predefined domain.
No column in any row contains multiple values.
In other words, any valid relational table is, by definition, in 1NF.

65. First Normal Form (1NF), cont’d
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

66. Second Normal Form (2NF)
A table is in 2NF if:
It is in 1NF.
It does not contain partial functional dependencies.

67. Second Normal Form (2NF), cont’d
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

68. Third Normal Form (3NF) A table is in 3NF if: It is in 2NF.
It does not contain transitive functional dependencies.

69. Third Normal Form (3NF), cont’d
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

70. Third Normal Form (3NF), cont’d
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

71. Third Normal Form (3NF), cont’d
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

72. Third Normal Form (3NF), cont’d
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

73. Another Normalization Example
Original table
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

74. Another Normalization Example, cont’d
2NF
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

75. Another Normalization Example, cont’d
3NF
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

76. Another Normalization Example, cont’d
Normalized
tables
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

77. Normalization vs. Denormalization
Normalization spreads data out over more tables.
The result is slower performance.
Sometimes it make sense to denormalize in order to improve performance.

78. Normalization vs. Denormalization, cont’d
Original table
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

79. Normalization vs. Denormalization, cont’d
Suppose the query is to retrieve for each campaign: AdCampaignID, AdCampaignName, CampaignMgrID, CampaignMgrName, ModeID, Media, Range, and BudgetPctg.
It is more efficient to retrieve this data from the original table than from the several normalized tables.

80. Normalization vs. Denormalization, cont’d
Database Systems
by Jukić, Vrbsky, & Nestorov
Pearson 2014
ISBN

81. Assignment #3
Create the first draft of the logical data model (relational schema) for your project.
Map your ER diagram from Assignment #2.
You can modify the diagram or create a new one.
Implement your relational model as a physical model by creating a MariaDB database.
Normalize your tables to 3NF.
Populate the tables with some sample data.

82. Assignment #3, cont’d
Create a zip file named after your team that contains:
Latest requirements.
Latest ER diagram.
Relational schema (created using ERDPlus).
Dump of your implemented MariaDB database.
Submit to Canvas: Assignment #3
Due Tuesday, February 28 at 11:59 PM